NMFLRED: Neuro-Multilevel-Fuzzy Logic RED Approach for Congestion Control in TCP/IP Differentiated Services


  • Sunil Kumar Kushwaha Medi-Caps University, Department of Computer Science and Engineering Indore (Madhya Pradesh), India
  • Suresh K. Jain Medi-Caps University, Department of Computer Science and Engineering Indore (Madhya Pradesh), India


Congestion, Fuzzy, Neural, DSCP, Delay, Packet loss, RED


The problem of congestion is ubiquitous and will remain forever, which is not limited to the field of networking but also in another field. The rapid expansion of the internet especially after corona pandemic in addition to the development of new computer technology such as ChatGPT, real time video and audio have accelerated the exponential increase in high-speed computer networks. The number of computers supporting more and more applications using the network has led to a significant increase in the number of packets passing across those networks, which has resulted in resource contention and ultimately leads to congestion. Thus, a solution needs to be drawn out which works for different prioritize packet with different forwarding and dropping probabilities. A neural network for self-adaptive and learning while Fuzzy logic for multi-variable linguistic calculation is used to generate an algorithm which is proposed in this research paper. A method using Multilevel Dropping method for different types of packets has been proposed which clearly shows that overall performance has been largely increased.


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Chandra, E. and B. Subramani, A Survey on Congestion Control. Global Journal of Computer Science and Technology 2010. 9(9): p. 82-87.

Thiruchelvi, G. and J. Raja, A Survey On Active Queue Management Mechanisms. IJCSNS International Journal of Computer Science and Network Security, 2008. 8(12): p. 130-145.

Bazaz, Y., S. kumar, and S. Anand, Congestion Control Mechanismusing Fuzzy Logic. International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), 2013. 2(3).

M, K. and S. G, Congestion control approach based on effective random early detection and fuzzy logic. MAGNT, 2015. 3(8): p. 180-193.

BAKLIZI, M., et al., Markov-Modulated Bernoulli Dynamic Gentle Random Early Detection. Journal of Theoretical and Applied Information Technology, 2018. 96(20).

Abdel-jaber, H., F. Thabtah, and M. Woodward. Traffic Management for the Gentle Random Early Detection using Discrete-Time Queueing. in Information Management in Modern Organizations: Trends & Challenges 2008.

Negnevitsky, M., Artificial Intelligence: A Guide to Intelligent Systems. Second Edition ed. 2005, England.

Stanojevic, R., R.N. Shorten, and C.M. Kellett, Adaptive tuning of drop-tail buffers for reducing queueing delays. Communications Letters, IEEE, 2006. 10(7): p. 570-572.

Brandauer, C., et al., Comparison of Tail Drop and Active Queue Management Performance for Bulk-Data and Web-Like Internet Traffic, in Proceedings of the Sixth IEEE Symposium on Computers and Communications. 2001, IEEE Computer Society.

Baklizi, M., et al., DYNAMIC STOCHASTIC EARLY DISCOVERY: A NEW CONGESTION CONTROL TECHNIQUE TO IMPROVE NETWORKS PERFORMANCE. International Journal of Innovative Computing, Information and Control, 2013. 9(4).

Welzl, M., Network Congestion Control: Managing Internet Traffic. 1 ed. 2005.

Kalav, D. and S. Gupta, Congestion Control in Communication Network Using RED, SFQ and REM Algorithm. International Refereed Journal of Engineering and Science (IRJES), 2012. 12 (2): p. 41-45.

Fan, X., et al. QBLUE: A New Congestion Control Algorithm Based on Queuing Theory. in High Performance Computing and Communication & 2012 IEEE 9th International Conference on Embedded Software and Systems (HPCC-ICESS), 2012 IEEE 14th International Conference on. 2012.

Floyd, S. and V. Jacobson, Random early detection gateways for congestion avoidance. IEEE/ACM Trans. Netw., 1993. 1(4): p. 397-413.

Floyd, S. Recommendations On Using the Gentle Variant of RED. http://www.aciri.org/floyd/red/gentle.html 2000.

Baklizi, M., et al., Performance Assessment of AGRED, RED and GRED Congestion Control Algorithms. Information Technology Journal, 2012. 11(2): p. 255-261.

Baklizi, M. and J. Ababneh, Performance Evaluation of the Proposed Enhanced Adaptive Gentle Random Early Detection Algorithm in Congestion Situations International Journal of Current Engineering and Technology 2016. 6(5).

Baklizi, m., et al., Fuzzy Logic Controller of Gentle Random Early Detection Based on Average Queue Length and Delay Rate International Journal of Fuzzy Systems, 2014. 16(1).

Abu-Shareha, A.A., Enhanced Random Early Detection using Responsive Congestion IndicatorsInternational Journal of Advanced Computer Science and Applications(IJACSA), 2019. 3(1): p. 358-367.

Abualhaj, M.M., A.A. Abu-Shareha, and M.M. Al-Tahrawi, FLRED: an efficient fuzzy logic based network congestion control method. The Natural Computing Applications, 2016.

Mulla, A.S. and B.T. Jadhav, Fuzzy Based Queue Management Policies–An Experimental Approach. International Journal of Current Engineering and Technology 2014. 4(1).

Baklizi, M., J. Ababneh, and N. Abdallah. Performance Investigations of Flred and Agred Active Queue Management Methods. in Proceedings of Academicsera 13 th International Conference. 2018. Istanbul, Turkey.

Seifaddini, O., A. Abdullah, and A.H. Vosough, Red, Gred, Agred Congestion Control Algorithms in Heterogeneous Traffic Types, In International Conference on Computing And Informatics. 2013.

Baklizi, M., J. Ababneh, and A Survey in Active Queue Management Methods According to Performance Measures. International Journal of Computer Trends and Technology (IJCTT), 2016. 38 (3): p. 145.

Tassiulas, L., Y.C. Hung, and S.S. Panwar, Optimal buffer control during congestion in an ATM network node. Networking, IEEE/ACM Transactions on, 1994. 2(4): p. 374-386.

Kusumawardani, M., Active queue management (aqm) and adaptive neuro fuzzy inference system (anfis) as intranet traffic Control. Academic Research International 2013. 4(5).

Ingoley, S.N. and M. Nashipudi, A Review: Fuzzy Logic in Congestion Control of Computer Network in International Conference in Recent Trends in Information Technology and Computer Science 2012.

Abdel-Jaber, H., et al., Performance evaluation for DRED discrete-time queueing network analytical model. J. Netw. Comput. Appl., 2008. 31(4): p. 750-770.

Woodward, M.E., Communication and Computer Networks: Modelling with discrete-time queues. 1993: Wiley-IEEE Computer Society Press.

Ababneh, j., et al., Derivation of Three Queue Nodes Discrete-Time Analytical Model Based on DRED Algorithm, in The Seventh IEEE International Conference on Information Technology: New Generations (ITNG 2010),USA.2010.2010.

Guan, L., et al., Discrete-time performance analysis of a congestion control mechanism based on RED under multi-class bursty and correlated traffic. Journal of Systems and Software, 2007. 80(10): p. 1716-1725.

D. M. Lopez-Pacheco, C. Pham, “Robust Transport Protocol for Dynamic High-Speed Networks: enhancing the XCP approach” ICON, 2022. http://web.univ-pau.fr/~cpham/Paper/icon05.pdf

E. Altman, K. Avrachenkov, C. Barakat, A.A. Kherani, B.J. Prabhu “Analysis of MIMD congestion control algorithm for High Speed Networks” Computer Networks: The International Journal of Computer and Telecommunications Networking Volume 48 , Issue 6, pp.: 972 – 989, 2021

Sumitha Bhandarkar, Saurabh Jain and A. L. Narasimha Reddy, “Improving TCP Performance in High Bandwidth High RTT Links Using Layered Congestion Control”, International Workshop on Protocols for Fast Long-Distance Networks, February 2022. http://whitepapers.silicon.com/0,39024759,60303395p,00.htm

Cheng Jin David X. Wei Steven H. Low, “FAST TCP: Motivation, Architecture, Algorithms, Performance” IEEE Infocom 2004. http://www.ieee-infocom.org/2019/Papers/52_2.PDF




How to Cite

Kushwaha, S. K. ., & Jain, S. K. . (2023). NMFLRED: Neuro-Multilevel-Fuzzy Logic RED Approach for Congestion Control in TCP/IP Differentiated Services. International Journal of Intelligent Systems and Applications in Engineering, 12(2), 674–685. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4312



Research Article